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Vehicle location deviation could easily occur in complex road network of open-pit mines, which seriously affect production vehicle route planning and intelligent scheduling.In this light, this study proposed a method for high-precision positioning and rectification of open pit minecars based on an improved HMM(Hiddden Markov Model).Specifically, this study clipped the road section by the complex road network map of the constructed open-pit mine, and cleaned the positioning trajectory data of the minecart, whose density was sparsed and segmented; a buffer zone was established to search for candidate road points of the trajectory, so as to improve the efficiency of the minecart positioning and correction under the complex road network; the HMM optimization model of positioning deviation correction was established by calculating the positioning observation probability and transition probability of the minecart.The optimal deviation correctionwas conducted in combination with the Viterbi algorithm to achieve high-precision positioning and deviation correction of the open-pit minecart.Results indicate that the method produces a better correction effect than the original HMM positioning correction method, the correction accuracy can reach 89.2 %, and the average correction time is only 0.055 s.This could effectively correct the positioning coordinates of open-pit mine vehicles under complex backgrounds.

In order to solve the problems of a slow solving speed and easily falling into the local optimization of an ore-blending process model (of polymetallic multiobjective open-pit mines), an efficient ore-blending scheduling optimization method based on multiagent deep reinforcement learning is proposed. Firstly, according to the actual production situation of the mine, the optimal control model for ore blending was established with the goal of minimizing deviations in ore grade and lithology. Secondly, the open-pit ore-matching problem was transformed into a partially observable Markov decision process, and the ore supply strategy was continuously optimized according to the feedback of the environmental indicators to obtain the optimal decision-making sequence. Thirdly, a multiagent deep reinforcement learning algorithm was introduced, which was trained continuously and modeled the environment to obtain the optimal strategy. Finally, taking a large open-pit metal mine as an example, the trained multiagent depth reinforcement learning algorithm model was verified via experiments, with the optimal training model displayed on the graphical interface. The experimental results show that the ore-blending optimization model constructed is more in line with the actual production requirements of a mine. When compared with the traditional multiobjective optimization algorithm, the efficiency and accuracy of the solution have been greatly improved, and the calculation results can be obtained in real-time. [ABSTRACT FROM AUTHOR]

Negative obstacles such as potholes and road collapses on unstructured roads in open-pit mining areas seriously affect the safe transportation of autonomous trucks. In this paper, we propose a real-time negative obstacle detection method for self-driving trucks in open-pit mines. By analyzing the characteristics of road negative obstacles in open-pit mines, a real-time target detection model based on the Yolov4 network was built. It uses RepVGG as the backbone feature extraction network, applying SimAM space and a channel attention mechanism to negative obstacle multiscale feature fusion. In addition, the classification and prediction modules of the network are optimized to improve the accuracy with which it detects negative obstacle targets. A non-maximum suppression optimization algorithm (CIoU Soft Non-Maximum Suppression, CS-NMS) is proposed in the post-processing stage of negative obstacle detection. The CS-NMS calculates the confidence of each detection frame with weighted optimization to solve the problems of encountering obscure negative obstacles or poor positioning accuracy of the detection boxes. The experimental results show that this research method achieves 96.35% mAP for detecting negative obstacles on mining roads with a real-time detection speed of 69.3 fps, and that it can effectively identify negative obstacles on unstructured roads in open-pit mines with complex backgrounds. [ABSTRACT FROM AUTHOR]

Background: Dermatofibrosarcoma protuberans (DFSP) is a kind of low‐grade malignant spindle cell neoplasm, the diagnosis, and treatment, which have markedly attracted clinicians' attention for its repeated recurrence. High‐resolution magnetic resonance imaging (HR‐MRI) has shown unique capabilities in diagnosis of various cutaneous tumors. Materials and methods: Data of 29 patients with clinically suspected DFSPs and undergoing dynamic contrast‐enhanced (DCE) HR‐MRI preoperatively were prospectively collected. The HR‐MRI qualitative features were evaluated and compared. The DCE‐associated quantitative parameters and the time‐signal intensity curve (TIC) types were provided using DCE sequences. Results: A total of 7 DFSPs, nine dermatofibromas (DF, including four cases of cellular variant [CDF]), 12 keloids, and one nodular fasciitis were enrolled. DFSP showed the largest major diameter and the deepest depth. Five DFSPs (71.4%) showed ill‐defined margins as well as infiltration of peripheral adipose. All DFSPs showed irregular shape. Most DFSPs presented hyperintensity on T2WI (71.4%) and iso‐intensity on T1WI (85.7%). Six cases (85.7%) had significant enhancement, and six cases (85.7%) had homogeneous enhancement. There were significant differences of Ktrans, Kep, Ve and iAUC values among DFSPs, DFs, and keloids, and DFSP had the highest values for these parameters. Six DFSPs (85.7%) and four CDFs (100%) showed type‐III TICs, while the other lesions showed type‐Ⅰor type‐Ⅱ TICs. Conclusions: DCE‐HR‐MRI could show the growth characteristics of DFSPs, which was of great value for the diagnosis and differential diagnosis of DFSPs and was helpful for the determination of treatment options, thereby to improve the prognosis of patients. [ABSTRACT FROM AUTHOR]